Lasse Klingbeil IPSN 2008Tim Wark

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Presenter: Shekhar Gupta

Most of the Localization Methods Work for Static NodeUses Range Estimation ( Requires More H/W)

Localization of Mobile Node is ImportantTransport ManagementMilitary Domain Health Services

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GPS

Cannot work

indoor

Size

Power Consumption Cost

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Fine Grained:

1. Radio Signal Strength2. Time Difference on Arrival3. Angle of Arrival

Disadvantages:

1. Extensive Computation2. Relatively Higher Hardware Cost3. High Energy4. Size

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Localization for Human Monitoring in Indoor

Uses Probabilistic Model to Estimate Location

Range Free Algorithm

Combines Data from Gyro Sensor and Previous State to Predict Next State

Uses Map Information to Get More Accurate Estimation

Deployed in Real World

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Protocol Design for Proposed Algorithm

Step Detection and Heading Measurement

Localization Based on Bayesian Estimation

Simulation and Experimental Results

Conclusion

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Static NodeAnchor Points in System

Mobile Node1. Nodes Need to be Localized2. Worn by people

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Sufficient Number of Nodes

Fixed Location

Send data packet to Base Node

Base Node is Connected to Gateway

Gateway Sends Data for Final Processing

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Always out of

Proximity

Always in the

Proximity

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Data Preprocessing1. Avoids Congestion2. Saves Energy

Reliable Communication

Uses Data Buffer

Inertial Sensor

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Localization Algorithm

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ack

Step Detection1. Data from Accelerometer Sensor2. Heuristic Algorithm for Robustness

Heading (Angle Detection)1. Data from Gyro Sensor2. Integration not Good (Noise)3. Uses Complementary Filter

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Proximity InformationGiven by Static

Seed Nodes

Improved Mobility Information

Indoor Map InformationGo to Service

Point

Given by Onboard

Inertial Sensors

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Estimate Dynamic System’s State from Noisy Observation

State is Location of Mobile Object

p(xt|xt-1) is Process Model, how the system’s state changing

p(zt|xt) is Measurement Model, likelihood of making observation zt given location xt

Prediction

Correction

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Initial Prediction

Correction

Estimation

Prediction

Correction

Estimation

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Works for Non-Gaussian, Non-Linear SystemInformation from Sensor is incorporated for prediction

Map Information is used as Importance factor w[i] for accurate prediction.

Correction Step uses the proximity Information

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Generation of Random PathEvaluation of RMS Error

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Error is Independent of degree of irregularity

Advantage over RSS based Algorithm

Algorithm Deployed in Real Building

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Without Map With Map

Estimates Location of Mobile Node

Relevant Information Transferred to Central PC

Used Monte Carlo Based Estimation

Evaluated Performance in Real World

RMS Error of 1m

Maximum Error 3.5m

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Pros:

Bayesian Estimation Gives Pretty Accurate Results.Does not Require Much Hardware.Map Information and Inertial Sensor Data Increased Accuracy.Not Dependent on Correct Radio Propagation Models Like RSS Based Algorithm.

Cons:

If Base Node Dies, Whole System will Fail All Static Nodes are Always Awake, Consumes Lots of Energy

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Questions ???

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